Neural Network Learning: Theoretical Foundations

John Shawe-Taylor

Abstract

The scientific method aims to derive mathematical models that help us to understand and exploit phenomena, whether they be natural or human made. Machine learning, and more particularly learning with neural networks, can be viewed as just such a phenomenon. Frequently remarkable performance is obtained by training networks to perform relatively complex AI tasks. Despite this success, most practitioners would readily admit that they are far from fully understanding why and, more importantly, when the techniques can be expected to be effective. The need for a fuller theoretical analysis and understanding of their performance has been a major research objective for the last decade. Neural Network Learning: Theoretical Foundations reports on important developments that have been made toward this goal within the computational learning theory framework.